Search

This paper studies batch processing of core maintenance in hypergraph streams. We focus on updating the coreness of each vertex after the hypergraph evolves. Unlike existing works that mainly focus on exact coreness updates for the single hyperedge dynamic or approximate update, we propose the first known batch processing algorithms for exact core maintenance with insertions or deletions of multiple hyperedges. By proposing a hyperedge structure Joint Hyperedge Set, we tackle the challenges of quantifying the range of coreness change and finding potential vertices whose coreness may update. In addition, we accelerate coreness updates even further by finding structures that enable parallel execution. Extensive experiments illustrate the efficiency, scalability, and effectiveness of our batch core maintenance algorithms on real-world hypergraphs. It shows that our algorithms can be faster than the single hyperedge processing approaches by a factor of nearly half the number of hyperedges processed, and our parallel algorithms achieve linear acceleration with the increasing number of threads. [ABSTRACT FROM AUTHOR]

• Structural similarity metrics and learning techniques are introduced. • A method for classifying algorithms by network structure is proposed. • General applications and research directions of network embedding are promoted. Social networks have a plethora of applications, and analysis of these applications has been gaining much interest from the research community. The high dimensionality of social network data poses a significant obstacle in its analysis, leading to the curse of dimensionality. The mushrooming of representation learning in various research fields facilitates network representation learning (also called network embedding), which will help us address the above-mentioned issue. Structural Representation Learning aims to learn low-dimensional vector representations of high-dimensional network data, allowing maximal preservation of network structural information. This representation can then serve as a backbone for various network-based applications. First, we investigate the techniques used in network representation learning and similarity indices. We then categorize the representative algorithms into three types based on the network structural level used in their learning process. We also introduce algorithms for representation learning of edges, subgraphs, and the whole network. Finally, we introduce the evaluation metrics and the applications of network representation learning and promising future research directions. [ABSTRACT FROM AUTHOR]

With the prosperity of vehicular networks and intelligent transport systems, vast amount of data can be easily collected by vehicular devices from their users and widely spread in the vehicular networks for the purpose of solving large-scale machine learning problems. Hence how to preserve the data privacy of users during the learning process has become a public concern. To address this concern, under the celebrated framework of differential privacy (DP), we present in this paper a decentralized parallel stochastic gradient descent (D-PSGD) algorithm, called DP2-SGD, which can offer protection for privacy of users in vehicular networks. With thorough analysis we show that DP2-SGD satisfies (ε, δ)-DP while the learning efficiency is the same as D-PSGD without privacy preservation. We also propose a refined algorithm called EC-SGD by introducing an error-compensate strategy. Extensive experiments show that EC-SGD can further improve the convergence efficiency over DP2-SGD in reality. [ABSTRACT FROM AUTHOR]

We prove the existence of the modified wave operators for a scalar quasilinear wave equation satisfying the weak null condition. This is accomplished in three steps. First, we derive a new reduced asymptotic system for the quasilinear wave equation by modifying Hörmander's method. Next, we construct an approximate solution, by solving our new reduced system given some scattering data at infinite time. Finally, we prove that the quasilinear wave equation has a global solution which agrees with the approximate solution at infinite time. [ABSTRACT FROM AUTHOR]

Let g be a hyperbolic Kac-Moody algebra of rank 2, and set λ = Λ 1 − Λ 2 , where Λ 1 , Λ 2 are the fundamental weights. Denote by V (λ) the extremal weight module of extremal weight λ with v λ the extremal weight vector, and by B (λ) the crystal basis of V (λ) with u λ the element corresponding to v λ. We prove that (i) B (λ) is connected, (ii) the subset B (λ) μ of elements of weight μ in B (λ) is a finite set for every integral weight μ, and B (λ) λ = { u λ } , (iii) every extremal element in B (λ) is contained in the Weyl group orbit of u λ , (iv) V (λ) is irreducible. Finally, we prove that the crystal basis B (λ) is isomorphic, as a crystal, to the crystal B (λ) of Lakshmibai-Seshadri paths of shape λ. [ABSTRACT FROM AUTHOR]

Let 픤 be a hyperbolic Kac-Moody algebra of rank 2, and set , where are the fundamental weights for 픤; note that λ is neither dominant nor antidominant. Let 픹(λ) be the crystal of all Lakshmibai-Seshadri paths of shape λ. We prove that (the crystal graph of) 픹(λ) is connected. Furthermore, we give an explicit description of Lakshmibai-Seshadri paths of shape λ. [ABSTRACT FROM AUTHOR]

In a multiple-message broadcast, an arbitrary number of messages originate at arbitrary nodes in the network at arbitrary times. The problem is to disseminate all these messages to the whole network. This paper gives the first randomized distributed multiple-message broadcast algorithm with worst-case performance guarantee in wireless ad hoc networks employing the SINR interference model which takes interferences from all the nodes in the network into account. The network model used in this paper also considers the harsh characteristics of wireless ad hoc networks: there is no prior structure, and nodes cannot perform collision detection and have little knowledge of the network topology. Under all these restrictions, our proposed randomized distributed multiple-message broadcast protocol can deliver any message m to all nodes in the network in O ( D + k + log 2 ⁡ n ) timeslots with high probability, where D is the network diameter, k is the number of messages whose broadcasts overlap with m , and n is the number of nodes in the network. We also study the lower bound for randomized distributed multiple-message broadcast protocols. In particular, we prove that any uniform randomized algorithm needs Ω ( D + k + log 2 ⁡ n log ⁡ log ⁡ log ⁡ n ) timeslots to disseminate k messages initially stored at k nodes. [ABSTRACT FROM AUTHOR]

In multi-hop radio networks, such as wireless ad-hoc networks and wireless sensor networks, nodes employ a MAC (Medium Access Control) protocol such as TDMA to coordinate accesses to the shared medium and to avoid interference of close-by transmissions. These protocols can be implemented using standard node coloring. The ( Δ + 1 ) -coloring problem is to color all nodes in as few timeslots as possible using at most Δ + 1 colors such that any two nodes within distance R are assigned different colors, where R is a given parameter and Δ is the maximum degree of the modeled unit disk graph using R as a scaling factor. Being one of the most fundamental problems in distributed computing, this problem is well studied and there is a long chain of algorithms prescribed for it. However, all previous works are based on abstract models, such as message passing models and graph based interference models, which limit the utility of these algorithms in practice. In this paper, for the first time, we consider the distributed ( Δ + 1 ) -coloring problem under the more practical SINR interference model. In particular, without requiring any knowledge about the neighborhood, we propose a novel randomized ( Δ + 1 ) -coloring algorithm with time complexity O ( Δ log ⁡ n + log 2 ⁡ n ) . For the case where nodes cannot adjust their transmission power, we give an O ( Δ log 2 ⁡ n ) randomized algorithm, which only incurs a logarithmic multiplicative factor overhead. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

Abstract: Acyclic coloring problem is a specialized problem that arises in the efficient computation of Hessians. A proper edge coloring of a graph is called acyclic if there is no -colored cycle in . The acyclic edge chromatic number of is the least number of colors in an acyclic edge coloring of . Alon et al. conjectured that . In this paper, we consider the sufficient conditions for the planar graphs satisfying and . [Copyright &y& Elsevier]

Hypergraphs are instrumental in modeling complex relational systems that encompass a wide spectrum of high-order interactions among components. One prevalent analysis task is the properties estimation of large-scale hypergraphs, which involves selecting a subset of nodes and hyperedges while preserving the characteristics of the entire hypergraph. This paper aims to sample hypergraphs via random walks and is the first to perform unbiased random walks for sampling of nodes and hyperedges simultaneously in large-scale hypergraphs to the best of our knowledge. Initially, we analyze the stationary distributions of nodes and hyperedges for the simple random walk, and show that there is a high bias in both nodes and hyperedges. Subsequently, to eliminate the high bias of the simple random walk, we propose unbiased random walk strategies for nodes and hyperedges, respectively. Finally, a single joint walk schema is developed for sampling nodes and hyperedges simultaneously. To accelerate the convergence process, we employ delayed acceptance and history-aware techniques to assist our algorithm in achieving fast convergence. Extensive experimental results validate our theoretical findings, and the unbiased sampling algorithms for nodes and hyperedges have their complex hypergraph scenarios for which they are applicable. The joint random walk algorithm balanced the sampling applicable to both nodes and hyperedges. [ABSTRACT FROM AUTHOR]

As an important concept to depict the freshness of an information when it arrives at the destination, the Age-of-Information (AoI) and its relative scheduling works can be a very useful tool to aggregate the information from the end devices to the edge servers in Internet of Vehicles, which can strongly support those time-sensitive services in intelligent transportation system. However, most of the previous works about the AoI scheduling on edge computing are the centralized ones, rely on the reliable communications, and ignore the mobility of vehicles, which may not suit the dynamic environment in Internet of Vehicles. In this paper, we consider the AoI optimization problem in a more comprehensive vehicle model, and present a distributed AoI optimization algorithm based on a single hop end-to-edge wireless network. With an arbitrary power control technique, our algorithm reaches an asymptotically optimal performance on minimizing the expectation of AoI. Both of theoretical analysis and extensive simulations are conducted to verify the correctness and efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

It has long been an area of interest to identify important vertices in social networks. Closeness centrality is one of the most popular measures of centrality of vertices. Generally speaking, it measures how a node is close to all other nodes on average. However, closeness centrality measures the centrality from a global view. Consequently, in real-world networks that is normally composed by some communities connected, using closeness centrality may suffer from the flaw that local central vertices within communities are neglected. To resolve this issue, we propose a new centrality measure, Hierarchical Closeness Centrality (HCC), to depict the local centrality of vertices. Experiments show that comparing with closeness centrality, HCC is a better index in finding most influential vertices and community detection. Furthermore, we present a parallel algorithm for HCC computation, by well analyzing the independence between vertices in the computation procedure. Extensive experiments on real-world datesets demonstrate that the parallel algorithm can greatly reduce the computation time compared to trivial algorithms. [ABSTRACT FROM AUTHOR]

We examine the utility of multiple channels of communication in wireless networks under the SINR model of interference. The central question is whether the use of multiple channels can result in linear speedup, up to some fundamental limit. We answer this question affirmatively for the data aggregation problem, perhaps the most fundamental problem in sensor networks. To achieve this, we form a hierarchical structure of independent interest, and illustrate its versatility by obtaining a new algorithm with linear speedup for the node coloring problem. [ABSTRACT FROM AUTHOR]

The core number of vertices is a basic index depicting cohesiveness of a graph, and has been widely used in large-scale graph analytics. In this paper, we study the update of core numbers of vertices in dynamic graphs with edge insertions/deletions, which is known as the core maintenance problem. Different from previous approaches that just focus on the case of single-edge insertion/deletion and sequentially handle the edges when multiple edges are inserted/deleted, we investigate the parallelism in the core maintenance procedure. Specifically, we show that if the inserted/deleted edges constitute a matching, the core number update with respect to each inserted/deleted edge can be handled in parallel. Based on this key observation, we propose parallel algorithms for core maintenance in both cases of edge insertions and deletions. Extensive experiments are conducted to evaluate the efficiency, stability, parallelism and scalability of our algorithms on different types of real-world, synthetic graphs and temporal networks. Comparing with former approaches, our algorithms can improve the core maintenance efficiency significantly. [ABSTRACT FROM AUTHOR]

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain. [ABSTRACT FROM AUTHOR]

Person reidentification (ReID) is a challenging computer vision task for identifying or verifying one or more persons when the faces are not available. In ReID, the indistinguishable background usually affects the model's perception of the foreground, which reduces the performance of ReID. Generally, the background of the same camera is similar, whereas that of different cameras is quite different. Based on this finding, we propose a template-aware transformer (TAT) method which can learn intersample indistinguishable features by introducing a learnable template for the transformer structure to cut down the model's attention to regions of the image with low discrimination, including backgrounds and occlusions. In the multiheaded attention module of the encoder, this template directs template-aware attention to indistinguishable features of the image and gradually increases the attention to distinguishable features as the encoder block deepens. We also increase the number of templates using side information considering the characteristics of ReID tasks to adapt the model to backgrounds that vary significantly with different camera IDs. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation. [ABSTRACT FROM AUTHOR]

One-slot link scheduling is important for enhancing the throughput capacity of wireless sensor networks. It includes two aspects: maximum links scheduling (MLS) and maximum weighted links scheduling (MWLS). In this paper we propose two heuristic algorithms for the two NP-hard problems with obvious power assignments under the SINR (signal-to-interference-plus-noise-ratio) model. For MLS, we propose an algorithm MTMA (maximum tolerance and minimum affectance), which improves the currently best approximation algorithm by 28%–62% on average. For MWLS, we give an effective heuristic algorithm MWMA (maximum weighted and minimum affectance), which performs better on improving the throughput and reducing the running time. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a novel clustering protocol ECDS, which considers coverage and energy efficiency simultaneously. [•] We propose two new metrics CI p and CI a and for coverage performance. [•] ECDC has better performance compared with some other clustering protocols. [ABSTRACT FROM AUTHOR]

Abstract: One of the most important issues for wireless sensor networks is to get a long network lifetime without affecting either communication connectivity or sensing coverage. Many sensors that are deployed randomly in a dense sensor network in a redundant way waste a lot of energy. One effective way to save energy is to let only a subset of sensors work at any given time. In this paper, we mainly consider such a problem. Selecting the minimum number of connected sensor nodes that can provide k-coverage (k ≥1), i.e., selecting a subset S of working sensors, such that almost every point in the sensing region can be covered by at least k sensors and the sensors in S can form a connected communication subgraph. We propose a connected k-coverage working sets construction algorithm (CWSC) based on Euclidean distance to k-cover the sensing region while minimizing the number of working sensors. CWSC can produce different coverage degrees according to different applications, which can enhance the flexibility of the sensor network. Simulation results show that the proposed algorithm, which can conserve energy and prolong the lifetime of the sensor network, is better than the previous algorithms. [Copyright &y& Elsevier]

Minimizing latency and energy consumption is the prime objective of the design of data aggregation in battery-powered wireless networks. A tradeoff exists between the aggregation latency and the energy consumption, which has been widely studied under the protocol interference model. There has been, however, no investigation of the tradeoff under the physical interference model that is known to capture more accurately the characteristics of wireless interferences. When coupled with the technique of successive interference cancellation, by which a receiver may recover signals from multiple simultaneous senders, the model can lead to much reduced latency but increased energy usage. In this paper, we investigate the latency-energy tradeoff for data aggregation in wireless sensor networks under the physical interference model and using successive interference cancellation. We present theoretical lower bounds on both latency and energy as well as their tradeoff, and give an efficient approximation algorithm that can achieve the asymptotical optimum in both aggregation latency and latency-energy tradeoff. We show that our algorithm can significantly reduce the aggregation latency, for which the energy consumption is kept at its lowest possible level. [ABSTRACT FROM AUTHOR]

Scheduling wireless links under the SINR model has attracted increasing attention in the past few years [1-6, 8-15, 18-20, 23-25, 27, 28, 33-36, 39, 41, 42, 45, 46]. However, most of previous work did not account for the precedence constraint that might exist among the wireless links. Precedence constraints are common in data aggregation problems where a sensor can not send data to its parent node before it has received data from all of its children. Existing solutions to the so-called minimum latency aggregation scheduling problem [7, 16, 21, 26, 29, 30, 32, 40, 43, 44] mainly focus on specific tree topologies rooted at the sink node. In this paper, we study the minimum latency link scheduling problem for arbitrary directed acyclic networks under both precedence and SINR constraints. Our formulation allows multiple sinks, and each sensor may transmit data to more than one parent node. We first show that the problem is NP-hard, and then propose a linear power assignment based polynomial time approximation algorithm and a dynamic labeling based heuristic algorithm. We have carried out extensive simulations for both dense and sparse arbitrary directed acyclic networks. The simulation results show that: (1) compared with both uniform and linear power assignments based algorithms, we can achieve much shorter scheduling lengths using our proposed labeling algorithm, and (2) the dynamic labeling based heuristic algorithm can lead to significantly shorter scheduling lengths than the heuristic algorithm which does not use labeling. [ABSTRACT FROM AUTHOR]

Abstract: Given a universe containing elements and a collection of multisets or sets over , the multiset multicover (MSMC) problem or the set multicover (SMC) problem is to cover all elements at least a number of times as specified in their coverage requirements with the minimum number of multisets or sets. In this paper, we give various exact algorithms for these two problems with or without constraints on the number of times a multiset or set may be chosen. First, we show that the MSMC without multiplicity constraints problem can be solved in time and polynomial space, where is the maximum coverage requirement and denotes the total number of given multisets over . (The notation suppresses a factor polynomial in .) To our knowledge, this is the first known exact algorithm for the MSMC without multiplicity constraints problem. Second, by combining dynamic programming and the inclusion–exclusion principle, we can exactly solve the SMC without multiplicity constraints problem in time. Compared with two recent results, in [Q.-S. Hua, Y. Wang, D. Yu, F.C.M. Lau, Set multi-covering via inclusion–exclusion, Theoretical Computer Science, 410 (38–40) (2009) 3882–3892] and [J. Nederlof, Inclusion exclusion for hard problems, Master Thesis, Utrecht University, The Netherlands, 2008], respectively, ours is the fastest exact algorithm for the SMC without multiplicity constraints problem. Finally, by directly using dynamic programming, we give the first known exact algorithm for the MSMC or the SMC with multiplicity constraints problem in time and space. This algorithm can also be easily adapted as a constructive algorithm for the MSMC without multiplicity constraints problem. [Copyright &y& Elsevier]

Abstract: Set multi-covering is a generalization of the set covering problem where each element may need to be covered more than once and thus some subset in the given family of subsets may be picked several times for minimizing the number of sets to satisfy the coverage requirement. In this paper, we propose a family of exact algorithms for the set multi-covering problem based on the inclusion–exclusion principle. The presented ESMC (Exact Set Multi-Covering) algorithm takes time and space where is the maximum value in the coverage requirement set (The notation omits a factor). We also propose the other three exact algorithms through different tradeoffs of the time and space complexities. To the best of our knowledge, this present paper is the first one to give exact algorithms for the set multi-covering problem with nontrivial time and space complexities. This paper can also be regarded as a generalization of the exact algorithm for the set covering problem given in [A. Björklund, T. Husfeldt, M. Koivisto, Set partitioning via inclusion–exclusion, SIAM Journal on Computing, in: FOCS 2006 (in press, special issue)]. [Copyright &y& Elsevier]

Multi-agent decision making is a fundamental problem in edge intelligence. In this paper, we study this problem for IoT networks under the distributed Multi-Armed Bandits (MAB) model. Most of existing works for distributed MAB demand long-time stable networks connected by powerful devices and hence may not be suitable for mobile IoT networks with harsh IoT constraints. To meet the challenge of resource constraints in mobile IoT environment, we propose a lightweight and robust learning algorithm in a dynamic network allowing topology changes. In our model, each agent is assumed to have only limited memory and communicate with each other asynchronously. Moreover, we assume that the bandwidth for exchanging information is limited and each agent can transmit O (log 2 K) bits (K denotes the number of arms) per communication. Rigorous analysis shows that despite these harsh constraints, the best arm/option can be identified collaboratively by the agents and the algorithm converges efficiently. Extensive experiments illustrate that the proposed algorithm exhibits good efficiency and stability in mobile settings. [ABSTRACT FROM AUTHOR]

Coreness is an important index to reflect the cohesiveness of a graph. The problems of core computation in static graphs and core update in dynamic graphs, known as the core decomposition and core maintenance problems respectively, have been extensively studied in previous work. However, most of these work focus on unweighted graphs. Considering that graphs are weighted in a lot of realistic applications, it is indispensable to extend the coreness to weighted graphs and devise efficient algorithms for weighted core decomposition and weighted core maintenance. In this work, we present a new definition of weighted coreness for vertices in a weighted graph, by taking into account the weights of vertices, which makes the coreness in unweighted graph be a special case. We propose efficient algorithms for both weighted core decomposition and weighted core maintenance problems. The coreness of vertices can be computed in linear time by the proposed decomposition algorithm, while the proposed core maintenance algorithm can process multiple-edge insertions/deletions simultaneously, which greatly reduces the core update time. Comprehensive experiments on both realistic networks and temporal graphs exhibit our algorithms are efficient and scalable. [ABSTRACT FROM AUTHOR]

Given a seller with k types of items and n single-minded buyers, i.e., each buyer is only interested in a particular bundle of items, to maximize the revenue, the seller must assign some amount of bundles to each buyer with respect to the buyer's accepted price. Each buyer b i is associated with a value function v i (⋅) such that v i (x) is the accepted unit bundle price b i is willing to pay for x bundles. In this paper, we assume that bundles can be sold fractionally. The single-minded item selling problem is proved to be NP-hard. Moreover, we give an O (k) -approximation algorithm. For the online version, i.e., buyers come one by one and the decision must be made immediately on the arrival of each buyer, an O (k ⋅ (log ⁡ h + log ⁡ k)) -competitive algorithm is given, where h is the highest unit item price among all buyers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust communication-efficient decentralized learning algorithm, named RCEDL , to address data heterogeneity , communication heterogeneity and communication efficiency simultaneously in real-world scenarios. To the best of our knowledge, this is the first work to address the above challenges in a united framework. In detail, we design a compressed cross-gradient aggregation mechanism with delay to resolve the Non-IID issues, a blocking-resilient mechanism which allows receiving delayed parameters and gradients, and a communication-efficient mechanism including parameters compression and adaptive neighbors selection methods to reduce the communication cost as much as possible. In addition, we also provide convergence analysis of RCEDL and prove its convergence rate O (1 N K ) same with the state-of-the-art decentralized learning algorithms. Finally, we conduct extensive experiments to evaluate RCEDL algorithm on two widely used datasets CIFAR-10 and MNIST under different experimental settings. Compared with the state-of-the-art baseline methods, the proposed RCEDL is much more robust with higher accuracy and at least 3.4 × communication cost reduction under the heterogeneous environment. [ABSTRACT FROM AUTHOR]

Due to the imbalanced energy consumption among nodes in wireless sensor networks, some nodes die prematurely, which decreases the network lifetime. To solve this problem, existing clustering protocols usually construct unequal clusters by exploiting uneven competition radius. Taking their imperfection on designing the uneven competition radius and intercluster communication into consideration, this paper proposes an improved distributed unequal clustering protocol (IDUC) for wireless sensor networks, where nodes are energy heterogeneous and scattered unevenly. The cores of IDUC are the formation of unequal cluster topology and the construction of intercluster communication routing tree. Compared with previous protocols, IDUC is suitable for various network scenarios, and it can balance the energy consumption more efficiently and extend the lifetime of networks significantly. [ABSTRACT FROM AUTHOR]

Coverage is one of the key issues to achieve energy efficiency of a wireless sensor network. Sensor scheduling is one of the most important methods to solve coverage problems. It can ensure the coverage degree of a region and prolong the network lifetime. In this paper, we focus on the k -coverage scheduling problem to guarantee k -coverage sensing and network connectivity. We consider both deterministic and stochastic sensing models of the sensors and adapt the results of deterministic sensing model to solve the sensor scheduling problem under the stochastic sensing model. We use regular pentagons to divide the sensing disks to solve the k -coverage problem. Each sensor node runs a stochastic k -coverage-preserving scheduling algorithm to determine its work modes, and redundant nodes can enter into sleep mode, while active nodes ensure the coverage of the network. Theoretical analysis and simulation results show that our algorithm can reduce the number of active nodes and extend the network lifetime significantly while maintaining a given coverage degree. [ABSTRACT FROM AUTHOR]